Meteorological calculations for the study:
Humanitarian Need Drives Multilateral Disaster Aid
Lisa Maria Dellmuth (corr-auth), Frida A.-M. Bender, Aiden Robert Jönsson, Elisabeth Lio Rosvold, Nina von Uexkull
-----

This document prepared by: Aiden Robert Jönsson
Department of Meteorology, Stockholm University, 2020

### VALIDATION OF EM-DAT EXTREMES ###

Scripts for matching ERA-Interim (ERAI) reanalysis data to geocoded EMDAT events are included here. Since the disasters are on level 3 administrative regions, for each disaster subtype, there is a data frame at the disaster "list" level where each event is described, and a "locations" data frame including all locations affected by each disaster. Each event may have more than one location, and so meteorological data must be aggregated to the event level after matching from the location level.

The script "CDHR_validation_matchERAIdata.r" was used to match ERAI data in each location. The resulting data frames are also saved in pickle format, which are saved in the "matched_disaster_metdata" directory for use in the validation.

The script "CDHR_validation_aggregatemetdata.r" was used to aggregate location level data to the event level.

The script "CDHR_make_ERAI_distributions.py" takes the relevant land-only and north of 60°S ERAI values over the entire time period and converts them to vectors in pickle file format for easy handling.

The iPython notebook "CDHR_validation_ttestandplot.ipynb" allows the overall ERAI distributions in the "ERAI_distributions" directory to be tested against the matched EMDAT disaster meteorological values in the "matched_disaster_metdata" directory. The resulting plot is included in the article as Figure 1.

NOTE: The source data for geo-coded EMDAT disasters can be found here:
https://www.ciesin.columbia.edu/data/gdis-1960-2018/
